Lung Cancer (Diagnosis)ELUCIDNCT07848815
Risk-Based Lung Cancer Detection in COPD Patients
A study testing Risk-based prediction models in people with Lung Cancer (Diagnosis) and COPD (Chronic Obstructive Pulmonary Disease), run by Vejle Hospital at 1 site in Denmark.
Trial summary
The goal of the interventional study is to evaluate whether risk-based stratification using the PLCOm2012 model and a machine learning (ML) model can identify patients with chronic obstructive pulmonary disease (COPD) who are at high risk of developing lung cancer and may benefit from low-dose computed tomography… Read more about this study
- Who is it for?
- Ages 50 years and older
- Conditions the study is for
- Lung Cancer (Diagnosis), COPD (Chronic Obstructive Pulmonary Disease)
- What is being tested?
- Risk-based prediction models
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 1 siteDenmark
- Study dates
- 1 Jun 2026to Dec 2033
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Diagnosed with COPD
- Age ≥ 50 years
- Attendance at the outpatient Respiratory Medicine clinic, Vejle Hospital, University Hospital of Southern Denmark
- Smoking history (current or former smoker)
- Consent to translational research and biobank.
Who cannot join
- Previous diagnosis of lung cancer within the last five years.
- Active treatment for cancer within the last 12 months except non-melanoma skin cancer and carcinoma in situ cervicis uteri.
- Invasive methods to verify potential lung cancer not an option.
- Inability to provide informed consent.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Denmark1 site, 1 recruiting
- Recruiting
Vejle Hospital, University Hospital of Southern Denmark
Vejle, Region Syddanmark, 7100
Cecilie M. Jacobsen, MScEng, +4579406644, cecilie.mondrup.jacobsen@rsyd.dk
About this study
Official title: Early Lung Cancer Detection in High-Risk Patients With Risk-Based Machine Learning Models and Biomarkers
The goal of the interventional study is to evaluate whether risk-based stratification using the PLCOm2012 model and a machine learning (ML) model can identify patients with chronic obstructive pulmonary disease (COPD) who are at high risk of developing lung cancer and may benefit from low-dose computed tomography (LDCT) screening. The study population includes adults aged 50 years and older with COPD and a history of smoking attending an outpatient clinic.
The main question it aims to answer are:
Read more
- What is the incidence of histopathologically confirmed lung cancer following risk-based stratification?
Participants will:
- Undergo lung cancer risk assessment using the PLCOm2012 model and an ML-based model based on clinical and laboratory data
- Be referred for LDCT if classified as high-risk
- Continue standard care if classified as low-risk
- Be followed through electronic health records for up to six years to assess outcomes including lung cancer incidence, adherence to LDCT, time to imaging, healthcare utilization, costs, and safety
Read the detailed description
Lung cancer is a leading cause of cancer-related mortality in Denmark, largely due to late-stage diagnosis. Early detection through low-dose computed tomography (LDCT) has been shown to reduce mortality in trials such as the National Lung Screening Trial and the NELSON trial, but implementation remains limited due to cost, capacity, and risk of false-positive findings. Risk-based approaches may improve screening efficiency by identifying individuals at highest risk.
This prospective, single-centre interventional study evaluates the feasibility and clinical implementation of lung cancer risk stratification in patients with chronic obstructive pulmonary disease (COPD). A total of 1,000 patients will be enrolled from an outpatient clinic. At baseline, participants will undergo risk assessment using the PLCOm2012 model and a machine learning (ML)-based model based on clinical and laboratory data.
Patients identified as high-risk by either model will be referred for LDCT, while low-risk patients will continue standard care. If LDCT findings are suspicious, further diagnostic evaluation will be performed according to routine clinical practice. Participants will be followed through electronic health records for up to six years, with outcome assessment at 1, 2, and 6 years.
The primary outcome is the incidence of histopathologically confirmed lung cancer. Secondary outcomes include adherence to LDCT referral, time from risk assessment to LDCT, number needed to screen, healthcare utilization, healthcare costs, and safety, including adverse events related to diagnostic procedures.
- Being tested
- Risk-based prediction models
- Keywords
- Lung cancer, Chronic obstructive pulmonary disease, Machine learning, Prediction models, Biomarkers
- Medical subject headings
- Lung Neoplasms, Disease, Pulmonary Disease, Chronic Obstructive
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Risk-based prediction modelsOther
Patients will be stratified into high-risk and low-risk groups using both the Lung Cancer Risk Prediction Calculator for smokers (PLCOm2012) and an in-house developed machine learning model based on sex, age, smoking status, and laboratory data from routine blood sample analyses.
| Study group | Role | What they receive |
|---|---|---|
| COPD patients undergoing lung cancer risk assessment | Experimental | All enrolled patients with chronic obstructive pulmonary disease (COPD) will undergo lung cancer risk assessment using the PLCOm2012 model and an in-house developed machine learning model. Based on the model-derived risk score, patients will be stratified into high-risk and low-risk groups. Patients classified as high-risk will undergo further diagnostic evaluation for lung cancer according to the study protocol. Patients classified as low-risk will continue with standard of care management. Outcomes will be compared between risk groups to evaluate the feasibility and clinical utility of the model. Receives: Other: Risk-based prediction models |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of histopathologically confirmed lung cancers
Measured: From risk assessment to end of follow-up at 6 years
Secondary outcomes (10)
Proportion of high-risk patients who undergo LDCT after referral
Measured: From risk assessment to LDCT completion at 1 year
Number of COPD patients requiring risk assessment and LDCT screening to detect one case of lung cancer
Measured: From risk assessment to end of follow-up at 6 years
Time from risk assessment to completion of LDCT
Measured: From risk assessment to LDCT completion at 1 year
Number of diagnostic procedures per patient stratified by risk group
Measured: From risk assessment to end of follow-up at 6 years
Number of hospitalizations per patient stratified by risk group
Measured: From risk assessment to end of follow-up at 6 years
Number of outpatient visits per patient stratified by risk group
Measured: From risk assessment to end of follow-up at 6 years
Healthcare costs per detected lung cancer case stratified by risk group
Measured: From risk assessment to end of follow-up at 6 years
Healthcare costs per patient stratified by risk group
Measured: From risk assessment to end of follow-up at 6 years
Proportion of eligible COPD patients consenting to risk assessment
Measured: From risk assessment to end of follow-up at 6 years
Proportion of eligible COPD patients completing LDCT
Measured: From risk assessment to LDCT completion at 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Sep 2026
- Study start1 Jun 2026
- Primary completionDec 2027 (estimated)
- Study completionDec 2033 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Lung Cancer (Diagnosis)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Vejle Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Morten H. Borg, MD, PhD, Study director, Vejle Hospital, University Hospital of Southern Denmark
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07848815
- Sponsor's protocol ID
- ELUCID
- Acronym
- ELUCID
- Status last verified
- Sep 2026
- First submitted
- 23 Sep 2026
- Last updated
- 30 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Bang Henriksen M, Hansen TF, Jensen LH, Brasen CL, Borg M, Hilberg O, Lokke A. Lung cancer among outpatients with COPD: a 7-year cohort study. ERJ Open Res. 2024 Jul 22;10(4):00064-2024. doi: 10.1183/23120541.00064-2024. eCollection 2024 Jul.
PubMed 39040576
Study at a glance
From the registry record, last updated by the sponsor on 30 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Lung Cancer Detection in High-Risk Patients With Risk-Based Machine Learning Models and Biomarkers
- ClinicalTrials.gov ID
- NCT07848815
- Acronym / sponsor ID
- ELUCID / ELUCID
- Keywords
- Lung cancer, Chronic obstructive pulmonary disease, Machine learning, Prediction models, Biomarkers
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Vejle HospitalUniversity, hospital or other
- Study start
- 1 Jun 2026 (actual)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Dec 2033 (estimated)
- First posted
- 30 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of the interventional study is to evaluate whether risk-based stratification using the PLCOm2012 model and a machine learning (ML) model can identify patients with chronic obstructive pulmonary disease (COPD) who are at high risk of developing lung cancer and may benefit from low-dose computed tomography (LDCT) screening. The study population includes adults aged 50 years and older with COPD and a history of smoking attending an outpatient clinic.
The main question it aims to answer are:
- What is the incidence of histopathologically confirmed lung cancer following risk-based stratification?
Participants will:
- Undergo lung cancer risk assessment using the PLCOm2012 model and an ML-based model based on clinical and laboratory data
- Be referred for LDCT if classified as high-risk
- Continue standard care if classified as low-risk
- Be followed through electronic health records for up to six years to assess outcomes including lung cancer incidence, adherence to LDCT, time to imaging, healthcare utilization, costs, and safety
Detailed description
Lung cancer is a leading cause of cancer-related mortality in Denmark, largely due to late-stage diagnosis. Early detection through low-dose computed tomography (LDCT) has been shown to reduce mortality in trials such as the National Lung Screening Trial and the NELSON trial, but implementation remains limited due to cost, capacity, and risk of false-positive findings. Risk-based approaches may improve screening efficiency by identifying individuals at highest risk.
This prospective, single-centre interventional study evaluates the feasibility and clinical implementation of lung cancer risk stratification in patients with chronic obstructive pulmonary disease (COPD). A total of 1,000 patients will be enrolled from an outpatient clinic. At baseline, participants will undergo risk assessment using the PLCOm2012 model and a machine learning (ML)-based model based on clinical and laboratory data.
Patients identified as high-risk by either model will be referred for LDCT, while low-risk patients will continue standard care. If LDCT findings are suspicious, further diagnostic evaluation will be performed according to routine clinical practice. Participants will be followed through electronic health records for up to six years, with outcome assessment at 1, 2, and 6 years.
The primary outcome is the incidence of histopathologically confirmed lung cancer. Secondary outcomes include adherence to LDCT referral, time from risk assessment to LDCT, number needed to screen, healthcare utilization, healthcare costs, and safety, including adverse events related to diagnostic procedures.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| COPD patients undergoing lung cancer risk assessment | Experimental | Other: Risk-based prediction modelsAll enrolled patients with chronic obstructive pulmonary disease (COPD) will undergo lung cancer risk assessment using the PLCOm2012 model and an in-house developed machine learning model. Based on the model-derived risk score, patients will be stratified into high-risk and low-risk groups. Patients classified as high-risk will undergo further diagnostic evaluation for lung cancer according to the study protocol. Patients classified as low-risk will continue with standard of care management. Outcomes will be compared between risk groups to evaluate the feasibility and clinical utility of the model. |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Diagnosed with COPD
- Age ≥ 50 years
- Attendance at the outpatient Respiratory Medicine clinic, Vejle Hospital, University Hospital of Southern Denmark
- Smoking history (current or former smoker)
- Consent to translational research and biobank.
Key exclusion criteria (4 listed)
- Previous diagnosis of lung cancer within the last five years.
- Active treatment for cancer within the last 12 months except non-melanoma skin cancer and carcinoma in situ cervicis uteri.
- Invasive methods to verify potential lung cancer not an option.
- Inability to provide informed consent.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Risk-based prediction modelsPatients will be stratified into high-risk and low-risk groups using both the Lung Cancer Risk Prediction Calculator for smokers (PLCOm2012) and an in-house developed machine learning model based on sex, age, smoking status, and laboratory data from routine blood sample analyses. | Other | COPD patients undergoing lung cancer risk assessment |
Primary outcome
Number of histopathologically confirmed lung cancers
Time frame: From risk assessment to end of follow-up at 6 years
10 secondary outcomes
Proportion of high-risk patients who undergo LDCT after referral
Time frame: From risk assessment to LDCT completion at 1 year
Number of COPD patients requiring risk assessment and LDCT screening to detect one case of lung cancer
Time frame: From risk assessment to end of follow-up at 6 years
Time from risk assessment to completion of LDCT
Time frame: From risk assessment to LDCT completion at 1 year
Number of diagnostic procedures per patient stratified by risk group
Time frame: From risk assessment to end of follow-up at 6 years
Number of hospitalizations per patient stratified by risk group
Time frame: From risk assessment to end of follow-up at 6 years
Number of outpatient visits per patient stratified by risk group
Time frame: From risk assessment to end of follow-up at 6 years
Healthcare costs per detected lung cancer case stratified by risk group
Time frame: From risk assessment to end of follow-up at 6 years
Healthcare costs per patient stratified by risk group
Time frame: From risk assessment to end of follow-up at 6 years
Proportion of eligible COPD patients consenting to risk assessment
Time frame: From risk assessment to end of follow-up at 6 years
Proportion of eligible COPD patients completing LDCT
Time frame: From risk assessment to LDCT completion at 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Sep 2026.
- Background: PubMed 39040576Bang Henriksen M, Hansen TF, Jensen LH, Brasen CL, Borg M, Hilberg O, Lokke A. Lung cancer among outpatients with COPD: a 7-year cohort study. ERJ Open Res. 2024 Jul 22;10(4):00064-2024. doi: 10.1183/23120541.00064-2024. eCollection 2024 Jul.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Morten H. Borg, MD, PhD study director
Vejle Hospital, University Hospital of Southern Denmark
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Risk-based prediction models in people with Lung Cancer (Diagnosis) and COPD (Chronic Obstructive Pulmonary Disease), run by Vejle Hospital at 1 site in Denmark.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 30 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Denmark. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Cecilie M. Jacobsen, MScEng (+4579406644, cecilie.mondrup.jacobsen@rsyd.dk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of histopathologically confirmed lung cancers (time frame: From risk assessment to end of follow-up at 6 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07848815, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Sep 2026.